Flexible resampling

README.md

@@ -23,7 +23,7 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 40.03
+         value: 40.01
 ---
 
 # Wav2Vec2-Large-XLSR-53-Chuvash
@@ -82,7 +82,7 @@ from tqdm.auto import tqdm
 from datasets import load_metric
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
-# Download the raw data instead of using HF datasets to save space 
+# Download the raw data instead of using HF datasets to save disk space 
 data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/cv.tar.gz"
 filestream = urllib.request.urlopen(data_url)
 data_file = tarfile.open(fileobj=filestream, mode="r|gz")
@@ -105,16 +105,15 @@ def clean_sentence(sent):
     sent = " ".join(sent.split())
     return sent
 
-resampler = torchaudio.transforms.Resample(48_000, 16_000)
-
 targets = []
 preds = []
 
 for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
     row["sentence"] = clean_sentence(row["sentence"])
     speech_array, sampling_rate = torchaudio.load(clips_path + row["path"])
+    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
     row["speech"] = resampler(speech_array).squeeze().numpy()
-    
+
     inputs = processor(row["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
 
     with torch.no_grad():
@@ -128,7 +127,7 @@ for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
 print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
 ```
 
-**Test Result**: 40.03 %  
+**Test Result**: 40.01 %  
 
 
 ## Training